Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era
This book delves into the integration of computer vision, image processing, and deep learning. It explores various applications, challenges, and the role of these technologies in diverse fields. Each chapter offers in - depth analysis and practical solutions, making it a valuable resource for researchers and practitioners in the related areas.
Chapter 1: Acceleration of Image Processing and Computer Vision Algorithms
This chapter focuses on the need to speed up image processing and computer vision algorithms for time - critical applications. With the growth of big data and high - resolution images, HPC systems and hardware accelerators like GPU, TPU, and FPGA are crucial. For example, the GPU’s parallel processing ability can accelerate CNN algorithms. However, choosing the right accelerator is challenging. The chapter also lists leading cloud platforms for computer vision acceleration.
Chapter 2: Acceleration of Computer Vision and Deep Learning: Surveillance Systems
The authors discuss how computer vision is transforming the security and surveillance industry. Despite the prevalence of CCTV cameras, they have limitations. Deep learning techniques, such as object detection and semantic segmentation, can address these issues. For instance, in border security, computer vision systems can use facial recognition to prevent illegal entry. The chapter also explores applications like pedestrian detection and crowd analysis, highlighting the potential of deep learning in enhancing surveillance efficiency.
Chapter 3: Deep Learning Architecture for a Real - Time Driver Safety Drowsiness Detection System
The chapter aims to prevent road accidents caused by driver drowsiness. It reviews existing methods and proposes a system based on the VGG16 model with transfer learning. By monitoring the driver’s eye and mouth status, the model can detect drowsiness early. For example, if the eye is closed for more than 12 frames, an alarm is sent. Experiments on the Kaggle Yawn - Eye - Dataset show high precision, with the model achieving 98.29% accuracy, indicating its effectiveness in alerting drivers.
Chapter 4: Deep Learning - Based Computer Vision for Robotics
This chapter explores the role of computer vision in robotics. It covers the importance of vision in robotics, from aiding manual assembly to process control. Different robotic vision systems, such as mobile, manipulation, and data acquisition systems, are introduced. For example, in object detection and autonomous navigation, algorithms like YOLO V3 and SLAM are used. The chapter also discusses how deep learning enables robots to perform tasks like obstacle avoidance and object manipulation more effectively.
Chapter 5: Deep Learning for Emotion Recognition
This chapter focuses on emotion recognition using deep learning. It classifies emotion models into categorical, dimensional, and componential types. In facial emotion recognition, databases like CK+ and JAFFE are used. Different deep learning algorithms, such as autoencoders and CNNs, are applied. For example, an attentional convolutional network can focus on specific regions of a face to recognize emotions. The chapter also explores emotion recognition based on gestures, physiological signals, text, and speech, and multimodal approaches.
Chapter 6: Smart Surveillance System Using Deep Learning Approaches
The authors propose a smart surveillance system to address the shortcomings of traditional CCTV monitoring. The system uses deep learning algorithms and computer vision techniques to detect intruders in real - time. A CNN - based binary classification model is trained with a large dataset. For example, it can detect humans even if they wear masks. The system outperforms existing solutions like YOLOv3 in terms of accuracy and size, and it can send alerts immediately, reducing the risk of robberies.
Chapter 7: Role of Deep Learning in Image and Video Processing
This chapter delves into the significance of deep learning in image and video processing. It highlights the challenges like low - resolution images and complex backgrounds. For instance, in image processing, CNNs are used to extract features from images. In video processing, techniques such as object detection and human action recognition are employed. Deep learning helps address these challenges, improving the quality and analysis of images and videos.
Chapter 8: Gesture and Posture Recognition by Using Deep Learning
The chapter explores gesture and posture recognition with deep learning. Gestures, like a wave of the hand, are important for communication, especially for the hearing - impaired. Posture recognition is useful in healthcare and surveillance. However, challenges exist, such as non - standard backgrounds in gesture recognition. Deep learning can overcome these by enabling machines to differentiate hands from backgrounds and recognize complex gestures, as shown in various applications like sign language interpretation.
Chapter 9: Evolution of Deep Learning for Biometric Identification and Recognition
This chapter focuses on the use of deep learning in biometric recognition. Biometric features like fingerprints and facial features are unique for identification. Deep learning models, such as DeepFace and its various algorithms like VGG - Face and Google FaceNet, have been applied to improve accuracy. For example, in face recognition, these models analyze facial landmarks. The chapter also discusses issues like creating larger datasets and security concerns in biometric systems.
Chapter 10: Hand - Crafted Feature Extraction and Deep Learning Models for Leaf Image Recognition
In this chapter, the authors propose a leaf recognition framework. Traditional methods using low - level features have limitations, so global features are incorporated. For example, SIFT is used for low - level feature extraction, and CNNs for high - level features. Two deep network models, Densenet and Xception, are experimented with. The results show that the proposed framework, which combines local and global features, performs well in recognizing leaf images, even under different illuminations and angles.
Chapter 11: The Evolution of Image Denoising From Model - Driven to Machine Learning: A Mathematical Perspective
The chapter presents the evolution of image denoising techniques. Model - driven approaches rely on probabilistic assumptions and prior knowledge, while learning - based approaches use neural networks. For example, in the Bayesian framework, data fidelity and regularization terms are crucial. The combination of both approaches, like in some recent studies, aims to achieve better denoising results. Experimental analysis shows that methods like ComplexNet and BUIFD perform well in removing noise from images.
Chapter 12: Machine Learning and Image Processing Based Computer Vision in Industry 4.0
This chapter explores the application of machine learning and computer vision in Industry 4.0. It discusses challenges such as purchasing strategies and workforce adaptation. In fault diagnosis of machinery, IoT and ML algorithms like Random Forest are used to detect motor failures. For predictive maintenance, ML techniques predict the remaining useful lifetime of machines. In conveyor speed monitoring, image processing and ML algorithms help overcome the drawbacks of traditional speed measurement methods, improving industrial efficiency.
Chapter 13: Real-Time Torpidity Detection for Drivers in Machine Learning Environments
This chapter presents a machine learning - based system to detect driver torpidity. Given that road accidents caused by driver fatigue are a serious issue—with India seeing about 1.5 lakh road deaths annually—it’s crucial to find a solution. The system uses a webcam to monitor eye movements. For example, if a driver’s eyes are closed for more than 4 seconds, an alarm sounds. If there’s no response, water is sprinkled. This offers a practical way to enhance road safety.
Chapter 14: Potential Market - Predictive Features Based Bitcoin Price Prediction Using Machine Learning Algorithms
The authors in this chapter aim to predict Bitcoin prices using machine learning. Bitcoin, a digital currency, has a volatile market. They preprocess data from 2012 - 2020, sourced from “Kaggle”, to reduce noise. By using algorithms like Long Short - Term Memory (LSTM), they identify patterns in the data. Their model forecasts prices for the next 30 days, providing valuable insights for investors.
Chapter 15: An Intelligent 8-Queen Placement Approach of Chess Game for Hiding 56-Bit Key of DES Algorithm Over Digital Color Images
This chapter proposes a steganographic approach. With the need for secure data transmission, the authors use the 8 - Queen placement algorithm from chess and Least Significant Bit (LSB) substitution. They divide the 56 - bit key of the DES algorithm into three parts and embed them in the red, green, and blue channels of a color image. Experiments show that this method effectively hides the key, ensuring secure communication.
Chapter 16: Deep Neural Network With Feature Optimization Technique for Classification of Coronary Artery Disease
Coronary artery disease (CAD) is a major global health concern. The chapter presents an integrated model of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Deep Neural Network (DNN) for CAD classification. Using datasets from the UC Irvine Machine Learning Repository, the model selects important features and improves classification accuracy. For example, the E - DNN model with FST GA - J48 shows high accuracy, sensitivity, and F1 - score.
Chapter 17: Detection and Classification of Diabetic Retinopathy Using Image Processing Algorithms, Convolutional Neural Network, and Signal Processing Techniques
This chapter focuses on detecting diabetic retinopathy. It proposes three approaches: image processing, convolutional neural network (CNN), and signal processing. In the image processing approach, fundus images are pre - processed, lesions are segmented, and features are extracted. The CNN approach classifies fundus images directly. The signal processing approach uses electro retinogram signals. Experiments show that the CNN approach has high accuracy, sensitivity, and specificity.
Chapter 18: Image Security Using Visual Cryptography
Visual cryptography is the focus of this chapter. It’s a method of secret sharing where a secret image is divided into shares. For example, in a 2 - out - of - 2 scheme, a binary secret image is split into two share images. These shares can be stacked to reveal the secret image. The chapter covers different types of visual cryptography, its implementation, and applications in areas like access control and copyright protection.
Chapter 19: Multipath Convolutional Neural Network for Brain Tumor Detection (CNN)
The authors in this chapter propose a multipath CNN architecture for brain tumor detection. Brain tumors are life - threatening, and existing methods have limitations. The proposed architecture combines local and global paths to analyze MRI images. It was tested on datasets like BRATS2013, BRATS2015, and BRATS2017. Results show that it outperforms existing methods in terms of dice index and timing, providing a more effective way to detect brain tumors.
Chapter 20: Image Enhancement Under Gaussian Impulse Noise for Satellite and Medical Applications
This chapter discusses image enhancement in the presence of Gaussian and impulse noise, which are common in satellite (e.g., hyperspectral imaging) and medical (e.g., MRI) applications. It reviews various denoising techniques, from filtering - based to learning - based methods. Experimental analysis on HSI and MRI data shows that different methods have different performances. For example, the Bayesian - HSI method performs well in hyperspectral image denoising.
Chapter 21: Algorithmic Approach for Spatial Entity and Mining
This chapter focuses on algorithms for spatial entity mining. Many databases hold both spatial and non - spatial information, yet existing systems have limitations in reducing fault cost and search time. The proposed algorithms aim to address these issues. The Trouble - Free Probing (TP) algorithm calculates the mass of each object in a dataset, recursively working through tree nodes. However, it’s inefficient for large datasets.
The Extended Branch and Bound (EBB) algorithm is an improvement for huge data sets. It prunes objects that can’t yield better results based on the upper bound mass of terminating entries in the tree. For instance, it skips accessing sub - trees when the higher bound of a node’s mass is not greater than a certain value, saving numerous mass computations.
The Extended Feature - Join (EFJ) algorithm is designed for multi - way spatial queries on feature trees. It projects feature points and their combinations to predict high - score spatial regions. By using a max - heap to manage combinations based on mass, it efficiently searches for relevant objects.
When applied to real - world data sets, such as those containing residential locations, schools, and stores, the EBB shows better execution time and tolerance cost compared to TP, while EFJ is optimal when dealing with small - sized feature data sets. These algorithms enhance the quality of data extraction and mining for spatial data.
Chapter 22: A Combined Feature Selection Technique for Improving Classification Accuracy
The chapter focuses on enhancing classification accuracy through a combined feature selection technique. By integrating methods like recursive feature elimination, chi - square, info - gain, and principal component analysis, the authors aim to reduce data dimensionality. Using three datasets related to skin disease and diabetes, they apply machine learning algorithms such as logistic regression and decision trees. The results show that the combined technique improves classifier accuracy. For example, in the diabetes dataset, the accuracy of the E4 ensemble with biomarker predictors reaches 94.8%, outperforming individual classifiers.
Chapter 23: Generalization and Efficiency on Finger Print Presentation Attack Anomaly Detection
This chapter delves into fingerprint presentation attack anomaly detection. Fingerprint recognition systems are vulnerable to attacks like gummy fingers and 3D - printed fingerprints. The authors evaluate the performance of a state - of - the - art CNN - based approach, Fingerprint Spoof Buster. They use 12 different presentation attack (PA) materials and analyze their material properties. For instance, Dragon Skin and Monster Liquid Latex are easily recognized even when not in the training set. The study shows that selecting a representative set of PA materials can improve the generalization performance of the detection system.
Chapter 24: Predictive Analytics on Female Infertility Using Ensemble Methods
With the increasing importance of predictive analytics in healthcare, this chapter proposes an intelligent prediction system for female infertility (PreFI). Using 26 variables and ensemble methods like bagging, boosting, and stacking, the researchers identify key variables as biomarkers. By analyzing a fertility clinic dataset, they develop four ensembles. For example, Ensemble E4, which combines three classifiers with biomarker predictors, achieves a high accuracy of 94.8%. This system can help doctors diagnose unexplained infertility earlier, potentially improving treatment outcomes.